An efficient domain-independent approach for supervised keyphrase extraction and ranking

arXiv:2404.07954v1 Announce Type: cross
Abstract: We present a supervised learning approach for automatic extraction of keyphrases from single documents. Our solution uses simple to compute statistical and positional features of candidate phrases and does not rely on any external knowledge base or on pre-trained language models or word embeddings. The ranking component of our proposed solution is a fairly lightweight ensemble model. Evaluation on benchmark datasets shows that our approach achieves significantly higher accuracy than several state-of-the-art baseline models, including …

abstract arxiv compute cs.cl cs.ir cs.lg documents domain embeddings extraction features independent knowledge knowledge base language language models ranking simple solution statistical supervised learning type word word embeddings